How to Visualize DocuSign Data in Python with pandas

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for DocuSign, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build DocuSign-connected Python applications and scripts for visualizing DocuSign data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to DocuSign data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live DocuSign data in Python. When you issue complex SQL queries from DocuSign, the driver pushes supported SQL operations, like filters and aggregations, directly to DocuSign and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to DocuSign Data

Connecting to DocuSign data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

To connect to DocuSign, set the following connection properties:

• UseSandbox: indicates whether current user account is sandbox or not (FALSE by default)
• AccountId (optional): set it in the connection string if you have access to multiple Account Ids

Authenticating to DocuSign

DocuSign uses the OAuth authentication standard. To authenticate using OAuth, you will need to create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the Help documentation more information.

Follow the procedure below to install the required modules and start accessing DocuSign through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize DocuSign Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with DocuSign data.

engine = create_engine("docusign:///?OAuthClientId=MyClientId& OAuthClientSecret=MyClientSecret& 
CallbackURL=http://localhost:33333&
InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to DocuSign

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT DocumentId, DocumentName FROM Documents WHERE DocumentName = 'TPSReport'", engine)

Visualize DocuSign Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the DocuSign data. The show method displays the chart in a new window.

df.plot(kind="bar", x="DocumentId", y="DocumentName")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for DocuSign to start building Python apps and scripts with connectivity to DocuSign data. Reach out to our Support Team if you have any questions.

Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("docusign:///?OAuthClientId=MyClientId& OAuthClientSecret=MyClientSecret& 
CallbackURL=http://localhost:33333&
InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT DocumentId, DocumentName FROM Documents WHERE DocumentName = 'TPSReport'", engine)

df.plot(kind="bar", x="DocumentId", y="DocumentName")
plt.show()